{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5dd8baf1",
   "metadata": {},
   "source": [
    "# 1.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "728f4e87",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>724 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "719           10      101             76             48      180  32.9   \n",
       "720            2      122             70             27        0  36.8   \n",
       "721            5      121             72             23      112  26.2   \n",
       "722            1      126             60              0        0  30.1   \n",
       "723            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "719                     0.171   63        0  \n",
       "720                     0.340   27        0  \n",
       "721                     0.245   30        0  \n",
       "722                     0.349   47        1  \n",
       "723                     0.315   23        0  \n",
       "\n",
       "[724 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据分析\n",
    "diabetes = pd.read_csv('./Desktop/diabetes2.csv')\n",
    "diabetes=diabetes.drop(\"Unnamed: 0\",axis=1)\n",
    "diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24e9da24",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
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       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
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       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
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       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
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       "      <th>711</th>\n",
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       "      <td>128</td>\n",
       "      <td>88</td>\n",
       "      <td>39</td>\n",
       "      <td>110</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.057</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.258</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "4              0      137             40             35      168  43.1   \n",
       "6              3       78             50             32       88  31.0   \n",
       "7              2      197             70             45      543  30.5   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "711            1      128             88             39      110  36.5   \n",
       "713            0      123             72              0        0  36.3   \n",
       "715            6      190             92              0        0  35.5   \n",
       "717            9      170             74             31        0  44.0   \n",
       "722            1      126             60              0        0  30.1   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "2                       0.672   32        1  \n",
       "4                       2.288   33        1  \n",
       "6                       0.248   26        1  \n",
       "7                       0.158   53        1  \n",
       "..                        ...  ...      ...  \n",
       "711                     1.057   37        1  \n",
       "713                     0.258   52        1  \n",
       "715                     0.278   66        1  \n",
       "717                     0.403   43        1  \n",
       "722                     0.349   47        1  \n",
       "\n",
       "[249 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#选择糖尿病患者数据\n",
    "diabetes_2=diabetes[diabetes[\"Outcome\"].isin([1])]\n",
    "diabetes_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01963373",
   "metadata": {},
   "source": [
    "# 2.特征选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc9aa1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>197</td>\n",
       "      <td>53</td>\n",
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       "      <th>...</th>\n",
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       "      <th>711</th>\n",
       "      <td>128</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>123</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>190</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>170</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>126</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose  Age\n",
       "0        148   50\n",
       "2        183   32\n",
       "4        137   33\n",
       "6         78   26\n",
       "7        197   53\n",
       "..       ...  ...\n",
       "711      128   37\n",
       "713      123   52\n",
       "715      190   66\n",
       "717      170   43\n",
       "722      126   47\n",
       "\n",
       "[249 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#选取影响力度较大的特征\n",
    "diabetes_1=diabetes_2[[\"Glucose\",\"Age\"]]\n",
    "diabetes_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc66f86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Glucose'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#所有数据点的可视化\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font',family='FangSong')#使其可显示中文\n",
    "sns.scatterplot(x=\"Age\",y=\"Glucose\",data=diabetes_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5d1349",
   "metadata": {},
   "source": [
    "# 3.标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b43d195e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.179661</td>\n",
       "      <td>1.140442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.346387</td>\n",
       "      <td>-0.481214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.187024</td>\n",
       "      <td>-0.391122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.153791</td>\n",
       "      <td>-1.021766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.813077</td>\n",
       "      <td>1.410718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Glucose       Age\n",
       "0  0.179661  1.140442\n",
       "1  1.346387 -0.481214\n",
       "2 -0.187024 -0.391122\n",
       "3 -2.153791 -1.021766\n",
       "4  1.813077  1.410718"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#标准化\n",
    "from sklearn.preprocessing import StandardScaler#标准化\n",
    "km=diabetes_1\n",
    "km_standardize=StandardScaler().fit_transform(km)\n",
    "km_standardize=pd.DataFrame(data=km_standardize,columns=list(km.columns))\n",
    "km_standardize.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f0e087",
   "metadata": {},
   "source": [
    "# 4.K值选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c84c9e8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.cluster import k_means# abc\n",
    "from sklearn.cluster import KMeans\n",
    "#存放每次结果的误差平方和\n",
    "cost=[]\n",
    "#尝试不同的聚类个数\n",
    "K=range(1,15)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k,random_state=99)\n",
    "    kmeanModel.fit(km_standardize)\n",
    "    cost.append(kmeanModel.inertia_)\n",
    "#肘部法则可视化\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('cost')\n",
    "plt.plot(K,cost,'o-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6dd2aec",
   "metadata": {},
   "source": [
    "# 5.模型建立"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d08bb056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5, random_state=99)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练k_means模型\n",
    "km_1 = KMeans(n_clusters=5,random_state=99)\n",
    "km_1.fit(km_standardize)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "657c6952",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Glucose'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将新标签组合到原来的数据框里面\n",
    "km_label = pd.DataFrame(km_1.labels_,columns=['新标签'])\n",
    "km = pd.concat([km,km_label],axis=1)\n",
    "#将聚类结果可视化\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "sns.scatterplot(x=\"Age\",y=\"Glucose\",hue=\"新标签\",data=km,palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5752dde9",
   "metadata": {},
   "source": [
    "# 6.聚类分析，对每一聚类进行进一步分析和描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d13c89fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Age</th>\n",
       "      <th>新标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>128.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>123.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>190.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>170.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>126.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose   Age  新标签\n",
       "0      148.0  50.0  2.0\n",
       "1        NaN   NaN  4.0\n",
       "2      183.0  32.0  3.0\n",
       "3        NaN   NaN  3.0\n",
       "4      137.0  33.0  2.0\n",
       "..       ...   ...  ...\n",
       "711    128.0  37.0  NaN\n",
       "713    123.0  52.0  NaN\n",
       "715    190.0  66.0  NaN\n",
       "717    170.0  43.0  NaN\n",
       "722    126.0  47.0  NaN\n",
       "\n",
       "[403 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final_df作为最后分析用的数据\n",
    "final_df=km\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0def624d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Age</th>\n",
       "      <th>新标签</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose   Age  新标签   BMI\n",
       "0    148.0  50.0  2.0  33.6\n",
       "1      NaN   NaN  4.0  26.6\n",
       "2    183.0  32.0  3.0  23.3\n",
       "3      NaN   NaN  3.0  28.1\n",
       "4    137.0  33.0  2.0  43.1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KMeans聚类结果增加一列“BMI”\n",
    "final_df[\"BMI\"]=diabetes[\"BMI\"]\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4a1bed1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>数目</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新标签</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>150.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>143.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>137.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>135.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose   Age   BMI  数目\n",
       "新标签                         \n",
       "0.0    150.0  40.0  33.0  41\n",
       "1.0    143.0  40.0  33.0  54\n",
       "2.0    143.0  33.0  34.0  34\n",
       "3.0    137.0  36.0  31.0  71\n",
       "4.0    135.0  39.0  33.0  49"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary_df用于对聚类的分析，下一步求平均值\n",
    "summary_df=final_df[[\"Glucose\",\"Age\",\"BMI\",\"新标签\"]].groupby(\"新标签\").mean().round()\n",
    "#求每一个类别中的数目\n",
    "summary_df['数目']=final_df.groupby(\"新标签\").size()\n",
    "#展示\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35e56e39",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid character in identifier (<ipython-input-29-5bbf0b51266b>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-29-5bbf0b51266b>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    根据描述分析，生成poi聚类结果画像，利用聚类，对人群标签进行精细划分，作为进一步商业决策的基础；\u001b[0m\n\u001b[1;37m                                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid character in identifier\n"
     ]
    }
   ],
   "source": [
    "根据描述分析，生成poi聚类结果画像，利用聚类，对人群标签进行精细划分，作为进一步商业决策的基础；\n",
    "由于BMI指数均在正常范围内，因此根据该结果\n",
    "可将糖尿病患者分为：\n",
    "年轻高血糖患者、年轻正常血糖患者、中年人高血糖患者、中年人正常糖尿病患者以及老年高糖尿病患者"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9781d806",
   "metadata": {},
   "source": [
    "# GMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8dddb8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import scipy.cluster.hierarchy as sch\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6f86a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>724 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "719           10      101             76             48      180  32.9   \n",
       "720            2      122             70             27        0  36.8   \n",
       "721            5      121             72             23      112  26.2   \n",
       "722            1      126             60              0        0  30.1   \n",
       "723            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "719                     0.171   63        0  \n",
       "720                     0.340   27        0  \n",
       "721                     0.245   30        0  \n",
       "722                     0.349   47        1  \n",
       "723                     0.315   23        0  \n",
       "\n",
       "[724 rows x 9 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./Desktop/diabetes2.csv')\n",
    "dataset=dataset.loc[:, ~dataset.columns.str.contains('^Unnamed')]\n",
    "dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1868ba01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148,  50],\n",
       "       [ 85,  31],\n",
       "       [183,  32],\n",
       "       ...,\n",
       "       [121,  30],\n",
       "       [126,  47],\n",
       "       [ 93,  23]], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.iloc[:, [1, 7]].values\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7d237e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMM预测结果：\n",
      " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class GMM:\n",
    "    def __init__(self,Data,K,weights = None,means = None,covars = None):\n",
    "        \"\"\"\n",
    "        这是GMM（高斯混合模型）类的构造函数\n",
    "        :param Data: 训练数据\n",
    "        :param K: 高斯分布的个数\n",
    "        :param weigths: 每个高斯分布的初始概率（权重）\n",
    "        :param means: 高斯分布的均值向量\n",
    "        :param covars: 高斯分布的协方差矩阵集合\n",
    "        \"\"\"\n",
    "        self.Data = Data\n",
    "        self.K = K\n",
    "        if weights is not None:\n",
    "            self.weights = weights\n",
    "        else:\n",
    "            self.weights  = np.random.rand(self.K)\n",
    "            self.weights /= np.sum(self.weights)        # 归一化\n",
    "        col = np.shape(self.Data)[1]\n",
    "        if means is not None:\n",
    "            self.means = means\n",
    "        else:\n",
    "            self.means = []\n",
    "            for i in range(self.K):\n",
    "                mean = np.random.rand(col)\n",
    "                #mean = mean / np.sum(mean)        # 归一化\n",
    "                self.means.append(mean)\n",
    "        if covars is not None:\n",
    "            self.covars = covars\n",
    "        else:\n",
    "            self.covars  = []\n",
    "            for i in range(self.K):\n",
    "                cov = np.random.rand(col,col)\n",
    "                #cov = cov / np.sum(cov)                    # 归一化\n",
    "                self.covars.append(cov)                     # cov是np.array,但是self.covars是list\n",
    "\n",
    "    def Gaussian(self,x,mean,cov):\n",
    "        \"\"\"\n",
    "        这是自定义的高斯分布概率密度函数\n",
    "        :param x: 输入数据\n",
    "        :param mean: 均值数组\n",
    "        :param cov: 协方差矩阵\n",
    "        :return: x的概率\n",
    "        \"\"\"\n",
    "        dim = np.shape(cov)[0]\n",
    "        # cov的行列式为零时的措施\n",
    "        covdet = np.linalg.det(cov + np.eye(dim) * 0.001)\n",
    "        covinv = np.linalg.inv(cov + np.eye(dim) * 0.001)\n",
    "        xdiff = (x - mean).reshape((1,dim))\n",
    "        # 概率密度\n",
    "        prob = 1.0/(np.power(np.power(2*np.pi,dim)*np.abs(covdet),0.5))*\\\n",
    "               np.exp(-0.5*xdiff.dot(covinv).dot(xdiff.T))[0][0]\n",
    "        return prob\n",
    "\n",
    "    def GMM_EM(self):\n",
    "        \"\"\"\n",
    "        这是利用EM算法进行优化GMM参数的函数\n",
    "        :return: 返回各组数据的属于每个分类的概率\n",
    "        \"\"\"\n",
    "        loglikelyhood = 0\n",
    "        oldloglikelyhood = 1\n",
    "        len,dim = np.shape(self.Data)\n",
    "        # gamma表示第n个样本属于第k个混合高斯的概率\n",
    "        gammas = [np.zeros(self.K) for i in range(len)]\n",
    "        while np.abs(loglikelyhood-oldloglikelyhood) > 0.00000001:\n",
    "            oldloglikelyhood = loglikelyhood\n",
    "            # E-step\n",
    "            for n in range(len):\n",
    "                # respons是GMM的EM算法中的权重w，即后验概率\n",
    "                respons = [self.weights[k] * self.Gaussian(self.Data[n], self.means[k], self.covars[k])\n",
    "                                                    for k in range(self.K)]\n",
    "                respons = np.array(respons)\n",
    "                sum_respons = np.sum(respons)\n",
    "                gammas[n] = respons/sum_respons\n",
    "            # M-step\n",
    "            for k in range(self.K):\n",
    "                #nk表示N个样本中有多少属于第k个高斯\n",
    "                nk = np.sum([gammas[n][k] for n in range(len)])\n",
    "                # 更新每个高斯分布的概率\n",
    "                self.weights[k] = 1.0 * nk / len\n",
    "                # 更新高斯分布的均值\n",
    "                self.means[k] = (1.0/nk) * np.sum([gammas[n][k] * self.Data[n] for n in range(len)], axis=0)\n",
    "                xdiffs = self.Data - self.means[k]\n",
    "                # 更新高斯分布的协方差矩阵\n",
    "                self.covars[k] = (1.0/nk)*np.sum([gammas[n][k]*xdiffs[n].reshape((dim,1)).dot(xdiffs[n].reshape((1,dim))) for n in range(len)],axis=0)\n",
    "            loglikelyhood = []\n",
    "            for n in range(len):\n",
    "                tmp = [np.sum(self.weights[k]*self.Gaussian(self.Data[n],self.means[k],self.covars[k])) for k in range(self.K)]\n",
    "                tmp = np.log(np.array(tmp))\n",
    "                loglikelyhood.append(list(tmp))\n",
    "            loglikelyhood = np.sum(loglikelyhood)\n",
    "        for i in range(len):\n",
    "            gammas[i] = gammas[i]/np.sum(gammas[i])\n",
    "        self.posibility = gammas\n",
    "        self.prediction = [np.argmax(gammas[i]) for i in range(len)]\n",
    "\n",
    "def run_main():\n",
    "\n",
    "    data = np.array(X.data)\n",
    "\n",
    "\n",
    "    # 对数据进行预处理\n",
    "    data = Normalizer().fit_transform(data)\n",
    "\n",
    "    # 解决画图是的中文乱码问题\n",
    "    mpl.rcParams['font.sans-serif'] = [u'simHei']\n",
    "    mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "    # GMM模型\n",
    "    K = 3\n",
    "    gmm = GMM(data,K)\n",
    "    gmm.GMM_EM()\n",
    "    y_pre = gmm.prediction\n",
    "    print(\"GMM预测结果：\\n\",y_pre)\n",
    "    plt.scatter(data[:, 0], data[:, 1], c=y_pre)\n",
    "    plt.title(\"GMM结果显示\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    run_main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ca8f34",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "05fd06d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "from collections import defaultdict,Counter\n",
    "\n",
    "# 加载数据集\n",
    "def loaddata(filepath):\n",
    "    points = [[float(eachpoint.split(\"\t\")[0]), float(eachpoint.split(\"\t\")[1])] for eachpoint in open(filepath,\"r\")]\n",
    "    return points\n",
    "\n",
    "# 以距离最大的维度上的距离为两个对象之间的距离\n",
    "def distance(point1,point2):\n",
    "    return max(abs(point1[0] - point2[0]),abs(point1[1] - point2[1]))\n",
    "\n",
    "\n",
    "# 计算每个数据点相邻的数据点，邻域定义为以该点为中心以边长为2*EPs的网格\n",
    "def getSurroundPoint(points,Eps=1):\n",
    "    surroundPoints = {}  # 每个元素默认是一个空列表\n",
    "    for idx1,point1 in enumerate(points):\n",
    "        for idx2,point2 in enumerate(points):\n",
    "            if (idx1 < idx2):\n",
    "                if(distance(point1,point2)<=Eps):\n",
    "                    surroundPoints.setdefault(idx1,[])   # 设置每个点的默认值邻节点为空列表\n",
    "                    surroundPoints.setdefault(idx2, [])   # 设置每个点的默认值邻节点为空列表\n",
    "                    surroundPoints[idx1].append(idx2)\n",
    "                    surroundPoints[idx2].append(idx1)\n",
    "    return surroundPoints\n",
    "\n",
    "\n",
    "\n",
    "# 定义邻域内相邻的数据点的个数大于4的为核心点，获取核心点以及核心点的周边点\n",
    "def findallCore(points,surroundPoints,Eps=10,MinPts=5):\n",
    "    # 获取所有核心点\n",
    "    corePointIdx = [pointIdx for pointIdx,surPointIdxs in surroundPoints.items() if len(surPointIdxs)>=MinPts]\n",
    "    # 邻域内包含某个核心点的非核心点，定义为边界点\n",
    "    borderPointIdx = []\n",
    "    for pointIdx,surPointIdxs in surroundPoints.items():\n",
    "        if (pointIdx not in corePointIdx):  # 边界点本身不是核心点\n",
    "            for onesurPointIdx in surPointIdxs:\n",
    "                if onesurPointIdx in corePointIdx:  # 边界点周边至少包含一个核心点\n",
    "                    borderPointIdx.append(pointIdx)\n",
    "                    break\n",
    "\n",
    "    corePoint = [points[pointIdx] for pointIdx in corePointIdx]  # 核心点\n",
    "    borderPoint = [points[pointIdx] for pointIdx in borderPointIdx]  #边界点\n",
    "    return corePointIdx,borderPointIdx\n",
    "\n",
    "# 获取所有噪声点。噪音点既不是边界点也不是核心点\n",
    "def findallnoise(points,corePointIdx,borderPointIdx):\n",
    "    noisePointIdx = [pointIdx for pointIdx in range(len(points)) if pointIdx not in corePointIdx and pointIdx not in borderPointIdx]\n",
    "    noisePoint = [points[pointIdx] for pointIdx in noisePointIdx]\n",
    "    return noisePoint\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 根据邻域关系，核心点，边界点进行分簇\n",
    "def divideGroups(points,surroundPoints,corePointIdx,borderPointIdx):\n",
    "    groups = [idx for idx in range(len(points))]  # groups记录每个节点所属的簇编号\n",
    "    # 各个核心点与其邻域内的所有核心点放在同一个簇中\n",
    "    for pointidx,surroundIdxs in surroundPoints.items():\n",
    "        for oneSurroundIdx in surroundIdxs:\n",
    "            if (pointidx in corePointIdx and oneSurroundIdx in corePointIdx and pointidx < oneSurroundIdx):\n",
    "                for idx in range(len(groups)):\n",
    "                    if groups[idx] == groups[oneSurroundIdx]:\n",
    "                        groups[idx] = groups[pointidx]\n",
    "\n",
    "    # 边界点跟其邻域内的某个核心点放在同一个簇中\n",
    "    for pointidx,surroundIdxs in surroundPoints.items():\n",
    "        for oneSurroundIdx in surroundIdxs:\n",
    "            if (pointidx in borderPointIdx and oneSurroundIdx in corePointIdx):\n",
    "                groups[pointidx] = groups[oneSurroundIdx]\n",
    "                break\n",
    "    return groups\n",
    "\n",
    "# 绘制分簇图\n",
    "def plotgroup(points,groups,noisePoint):\n",
    "    # 取簇规模最大的3个簇\n",
    "    finalGroup = Counter(groups).most_common(3)\n",
    "    finalGroup = [onecount[0] for onecount in finalGroup]\n",
    "    group1 = [points[idx] for idx in range(len(points)) if groups[idx]==finalGroup[0]]\n",
    "    group2 = [points[idx] for idx in range(len(points)) if groups[idx]==finalGroup[1]]\n",
    "    group3 = [points[idx] for idx in range(len(points)) if groups[idx]==finalGroup[2]]\n",
    "    pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for eachpoint in group1], 'or')\n",
    "    pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for eachpoint in group2], 'oy')\n",
    "    pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for eachpoint in group3], 'og')\n",
    "    # 打印噪音点，黑色\n",
    "    pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')\n",
    "    pl.show()\n",
    "\n",
    "\n",
    "if __name__=='__main__':\n",
    "    points = loaddata('./Desktop/age.txt')   # 加载数据\n",
    "    surroundPoints=getSurroundPoint(points,Eps=2)  # 获取邻域关系\n",
    "    corePointIdx, borderPointIdx = findallCore(points,surroundPoints,Eps=2,MinPts=3)  # 获取核心节点和边界节点\n",
    "    noisePoint = findallnoise(points,corePointIdx,borderPointIdx)  # 获取噪音节点\n",
    "    groups = divideGroups(points,surroundPoints,corePointIdx,borderPointIdx)   # 节点分簇\n",
    "    plotgroup(points, groups, noisePoint)  # 可视化绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb29d40",
   "metadata": {},
   "source": [
    "# 凝聚层次聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "28b2dfc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c2d8f637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>724 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "719           10      101             76             48      180  32.9   \n",
       "720            2      122             70             27        0  36.8   \n",
       "721            5      121             72             23      112  26.2   \n",
       "722            1      126             60              0        0  30.1   \n",
       "723            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "719                     0.171   63        0  \n",
       "720                     0.340   27        0  \n",
       "721                     0.245   30        0  \n",
       "722                     0.349   47        1  \n",
       "723                     0.315   23        0  \n",
       "\n",
       "[724 rows x 9 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./Desktop/diabetes2.csv')\n",
    "dataset=dataset.loc[:, ~dataset.columns.str.contains('^Unnamed')]\n",
    "dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "42ed5a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6. , 33.6],\n",
       "       [ 1. , 26.6],\n",
       "       [ 8. , 23.3],\n",
       "       ...,\n",
       "       [ 5. , 26.2],\n",
       "       [ 1. , 30.1],\n",
       "       [ 1. , 30.4]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.iloc[:, [0, 5]].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4ee1e201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148. ,  33.6],\n",
       "       [ 85. ,  26.6],\n",
       "       [183. ,  23.3],\n",
       "       ...,\n",
       "       [121. ,  26.2],\n",
       "       [126. ,  30.1],\n",
       "       [ 93. ,  30.4]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = dataset.iloc[:, [1, 5]].values\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c5217ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[72. , 33.6],\n",
       "       [66. , 26.6],\n",
       "       [64. , 23.3],\n",
       "       ...,\n",
       "       [72. , 26.2],\n",
       "       [60. , 30.1],\n",
       "       [70. , 30.4]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q = dataset.iloc[:, [2, 5]].values\n",
    "Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1bf9b1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[35. , 33.6],\n",
       "       [29. , 26.6],\n",
       "       [ 0. , 23.3],\n",
       "       ...,\n",
       "       [23. , 26.2],\n",
       "       [ 0. , 30.1],\n",
       "       [31. , 30.4]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = dataset.iloc[:, [3, 5]].values\n",
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d6eb7aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  [35.0, 35.0, 45.0, 45.0],\n",
       "  [25.0, 25.0, 40.0, 40.0],\n",
       "  [10.0, 10.0, 32.5, 32.5],\n",
       "  [65.0, 65.0, 75.0, 75.0],\n",
       "  [55.0, 55.0, 70.0, 70.0],\n",
       "  [85.0, 85.0, 95.0, 95.0],\n",
       "  [105.0, 105.0, 115.0, 115.0],\n",
       "  [90.0, 90.0, 110.0, 110.0],\n",
       "  [62.5, 62.5, 100.0, 100.0],\n",
       "  [21.25, 21.25, 81.25, 81.25],\n",
       "  [135.0, 135.0, 145.0, 145.0],\n",
       "  [125.0, 125.0, 140.0, 140.0],\n",
       "  [155.0, 155.0, 165.0, 165.0],\n",
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       "  [175.0, 175.0, 190.0, 190.0],\n",
       "  [160.0, 160.0, 182.5, 182.5],\n",
       "  [132.5, 132.5, 171.25, 171.25],\n",
       "  [205.0, 205.0, 215.0, 215.0],\n",
       "  [245.0, 245.0, 255.0, 255.0],\n",
       "  [235.0, 235.0, 250.0, 250.0],\n",
       "  [225.0, 225.0, 242.5, 242.5],\n",
       "  [275.0, 275.0, 285.0, 285.0],\n",
       "  [265.0, 265.0, 280.0, 280.0],\n",
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       "  [272.5, 272.5, 300.0, 300.0],\n",
       "  [233.75, 233.75, 286.25, 286.25],\n",
       "  [210.0, 210.0, 260.0, 260.0],\n",
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       "  [325.0, 325.0, 340.0, 340.0],\n",
       "  [315.0, 315.0, 332.5, 332.5],\n",
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       "  [370.0, 370.0, 390.0, 390.0],\n",
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       "  [445.0, 445.0, 455.0, 455.0],\n",
       "  [465.0, 465.0, 475.0, 475.0],\n",
       "  [485.0, 485.0, 495.0, 495.0],\n",
       "  [470.0, 470.0, 490.0, 490.0],\n",
       "  [450.0, 450.0, 480.0, 480.0],\n",
       "  [505.0, 505.0, 515.0, 515.0],\n",
       "  [525.0, 525.0, 535.0, 535.0],\n",
       "  [510.0, 510.0, 530.0, 530.0],\n",
       "  [465.0, 465.0, 520.0, 520.0],\n",
       "  [358.75, 358.75, 492.5, 492.5],\n",
       "  [545.0, 545.0, 555.0, 555.0],\n",
       "  [575.0, 575.0, 585.0, 585.0],\n",
       "  [595.0, 595.0, 605.0, 605.0],\n",
       "  [580.0, 580.0, 600.0, 600.0],\n",
       "  [565.0, 565.0, 590.0, 590.0],\n",
       "  [550.0, 550.0, 577.5, 577.5],\n",
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       "  [615.0, 615.0, 630.0, 630.0],\n",
       "  [655.0, 655.0, 665.0, 665.0],\n",
       "  [645.0, 645.0, 660.0, 660.0],\n",
       "  [622.5, 622.5, 652.5, 652.5],\n",
       "  [563.75, 563.75, 637.5, 637.5],\n",
       "  [685.0, 685.0, 695.0, 695.0],\n",
       "  [675.0, 675.0, 690.0, 690.0],\n",
       "  [735.0, 735.0, 745.0, 745.0],\n",
       "  [725.0, 725.0, 740.0, 740.0],\n",
       "  [715.0, 715.0, 732.5, 732.5],\n",
       "  [705.0, 705.0, 723.75, 723.75],\n",
       "  [682.5, 682.5, 714.375, 714.375],\n",
       "  [755.0, 755.0, 765.0, 765.0],\n",
       "  [775.0, 775.0, 785.0, 785.0],\n",
       "  [795.0, 795.0, 805.0, 805.0],\n",
       "  [780.0, 780.0, 800.0, 800.0],\n",
       "  [760.0, 760.0, 790.0, 790.0],\n",
       "  [698.4375, 698.4375, 775.0, 775.0],\n",
       "  [600.625, 600.625, 736.71875, 736.71875],\n",
       "  [425.625, 425.625, 668.671875, 668.671875],\n",
       "  [122.34375, 122.34375, 547.1484375, 547.1484375],\n",
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       "  [845.0, 845.0, 855.0, 855.0],\n",
       "  [835.0, 835.0, 850.0, 850.0],\n",
       "  [865.0, 865.0, 875.0, 875.0],\n",
       "  [885.0, 885.0, 895.0, 895.0],\n",
       "  [915.0, 915.0, 925.0, 925.0],\n",
       "  [905.0, 905.0, 920.0, 920.0],\n",
       "  [890.0, 890.0, 912.5, 912.5],\n",
       "  [870.0, 870.0, 901.25, 901.25],\n",
       "  [842.5, 842.5, 885.625, 885.625],\n",
       "  [945.0, 945.0, 955.0, 955.0],\n",
       "  [935.0, 935.0, 950.0, 950.0],\n",
       "  [975.0, 975.0, 985.0, 985.0],\n",
       "  [965.0, 965.0, 980.0, 980.0],\n",
       "  [995.0, 995.0, 1005.0, 1005.0],\n",
       "  [972.5, 972.5, 1000.0, 1000.0],\n",
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       "  [1095.0, 1095.0, 1105.0, 1105.0],\n",
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       "  [1493.75, 1493.75, 1540.0, 1540.0],\n",
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      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dendrogram = sch.dendrogram(sch.linkage(X, method='ward'))\n",
    "#dendrogram1 = sch.dendrogram(sch.linkage(Y, method='ward'))\n",
    "dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a1bab966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 4, 3, 0, 4, 1, 0, 2, 2, 4, 3, 3, 0, 2, 0, 1, 0, 2, 2, 1, 0,\n",
       "       1, 2, 3, 3, 2, 1, 1, 3, 4, 1, 1, 2, 1, 0, 0, 1, 2, 3, 1, 4, 0, 2,\n",
       "       1, 0, 3, 3, 3, 1, 0, 3, 0, 0, 2, 0, 2, 4, 1, 2, 1, 1, 2, 3, 1, 1,\n",
       "       1, 2, 1, 1, 2, 0, 3, 3, 1, 3, 2, 1, 0, 1, 0, 3, 4, 3, 0, 4, 3, 1,\n",
       "       1, 3, 1, 0, 2, 4, 4, 3, 2, 1, 4, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2,\n",
       "       3, 3, 0, 0, 1, 4, 2, 0, 0, 1, 1, 4, 3, 2, 1, 0, 3, 1, 1, 1, 2, 1,\n",
       "       4, 0, 1, 2, 1, 1, 1, 2, 3, 0, 4, 1, 0, 2, 0, 3, 3, 3, 0, 1, 0, 0,\n",
       "       3, 2, 3, 1, 2, 2, 3, 2, 1, 0, 3, 0, 2, 0, 2, 2, 4, 3, 4, 4, 1, 1,\n",
       "       0, 1, 1, 4, 0, 2, 3, 0, 4, 3, 0, 1, 3, 2, 3, 3, 0, 3, 1, 2, 1, 1,\n",
       "       3, 0, 1, 1, 1, 0, 0, 1, 3, 2, 1, 2, 1, 3, 1, 2, 0, 0, 0, 2, 0, 3,\n",
       "       2, 1, 2, 1, 1, 3, 4, 3, 1, 4, 3, 0, 3, 2, 0, 1, 3, 2, 4, 3, 1, 3,\n",
       "       1, 1, 3, 3, 1, 3, 1, 3, 2, 3, 0, 4, 0, 1, 4, 0, 2, 0, 3, 3, 4, 3,\n",
       "       2, 1, 3, 2, 4, 3, 0, 0, 3, 0, 1, 1, 0, 0, 4, 1, 1, 1, 1, 4, 1, 0,\n",
       "       2, 4, 0, 3, 3, 3, 1, 1, 0, 3, 3, 1, 1, 3, 2, 0, 4, 3, 1, 3, 1, 1,\n",
       "       3, 1, 2, 0, 1, 3, 3, 4, 3, 0, 2, 1, 2, 3, 3, 2, 2, 0, 3, 3, 2, 2,\n",
       "       1, 3, 2, 2, 0, 0, 0, 1, 2, 0, 2, 1, 1, 4, 3, 3, 1, 0, 1, 0, 0, 0,\n",
       "       3, 0, 2, 0, 1, 3, 3, 3, 3, 3, 1, 0, 1, 3, 1, 2, 3, 3, 2, 3, 1, 0,\n",
       "       4, 1, 2, 4, 0, 1, 2, 0, 2, 4, 4, 0, 0, 1, 1, 3, 1, 1, 3, 0, 4, 1,\n",
       "       0, 3, 0, 3, 0, 0, 1, 0, 1, 3, 3, 4, 3, 0, 2, 3, 2, 1, 3, 1, 2, 3,\n",
       "       0, 3, 0, 1, 3, 3, 2, 0, 1, 1, 4, 1, 0, 1, 3, 4, 0, 1, 4, 3, 3, 0,\n",
       "       0, 0, 1, 1, 2, 2, 1, 0, 3, 1, 1, 1, 1, 3, 0, 0, 0, 0, 3, 4, 1, 1,\n",
       "       1, 3, 4, 4, 3, 1, 1, 3, 0, 1, 0, 2, 1, 3, 3, 4, 1, 3, 4, 4, 3, 3,\n",
       "       1, 2, 2, 3, 1, 1, 2, 2, 3, 3, 3, 1, 4, 3, 2, 0, 1, 2, 4, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 4, 3, 1, 1, 0, 4, 0, 2, 2, 0, 0,\n",
       "       3, 2, 3, 0, 1, 1, 1, 2, 4, 1, 3, 1, 0, 3, 2, 4, 0, 0, 3, 3, 2, 3,\n",
       "       3, 2, 4, 1, 0, 0, 2, 3, 0, 3, 2, 3, 2, 3, 3, 1, 1, 1, 0, 3, 0, 3,\n",
       "       3, 1, 0, 1, 1, 4, 2, 3, 3, 0, 3, 2, 1, 2, 0, 4, 2, 2, 3, 4, 0, 4,\n",
       "       3, 4, 2, 2, 3, 0, 3, 3, 2, 2, 1, 0, 3, 0, 1, 3, 3, 1, 0, 4, 1, 1,\n",
       "       1, 0, 2, 1, 4, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 3, 1, 0, 2, 2, 4, 2,\n",
       "       2, 3, 3, 0, 0, 2, 1, 4, 3, 3, 0, 4, 2, 0, 0, 4, 3, 3, 3, 2, 1, 1,\n",
       "       1, 3, 0, 1, 2, 0, 3, 1, 0, 3, 2, 1, 0, 3, 0, 1, 3, 0, 1, 0, 2, 0,\n",
       "       1, 1, 4, 2, 3, 2, 0, 1, 4, 1, 1, 3, 3, 2, 0, 3, 3, 2, 0, 1, 0, 0,\n",
       "       1, 4, 2, 0, 3, 0, 1, 0, 0, 2, 2, 2, 1, 0, 4, 0, 1, 3, 2, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#利用欧几里得距离来创建AgglomerativeClustering的实例，其中该欧几里得距离是指点之间的距离和用于计算聚类的接近度的距离\n",
    "model = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')\n",
    "model.fit(X)\n",
    "model.fit(Y)\n",
    "model.fit(Q)\n",
    "model.fit(W)\n",
    "labels = model.labels_\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "62060871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#简写表示法将属于前三个类的所有样本显示为特定颜色\n",
    "plt.scatter(X[labels==0, 0], X[labels==0, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==1, 0], X[labels==1, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==2, 0], X[labels==2, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==3, 0], X[labels==3, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==4, 0], X[labels==4, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==5, 0], X[labels==5, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==6, 0], X[labels==6, 1], s=50, marker='o', color='red')\n",
    "plt.scatter(X[labels==7, 0], X[labels==7, 1], s=50, marker='o', color='red')\n",
    "\n",
    "plt.scatter(Y[labels==0, 0], Y[labels==0, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==1, 0], Y[labels==1, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==2, 0], Y[labels==2, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==3, 0], Y[labels==3, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==4, 0], Y[labels==4, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==5, 0], Y[labels==5, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==6, 0], Y[labels==6, 1], s=50, marker='o', color='blue')\n",
    "plt.scatter(Y[labels==7, 0], Y[labels==7, 1], s=50, marker='o', color='blue')\n",
    "\n",
    "plt.scatter(Q[labels==0, 0], Q[labels==0, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==1, 0], Q[labels==1, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==2, 0], Q[labels==2, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==3, 0], Q[labels==3, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==4, 0], Q[labels==4, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==5, 0], Q[labels==5, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==6, 0], Q[labels==6, 1], s=50, marker='o', color='yellow')\n",
    "plt.scatter(Q[labels==7, 0], Q[labels==7, 1], s=50, marker='o', color='yellow')\n",
    "\n",
    "plt.scatter(W[labels==0, 0], W[labels==0, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==1, 0], W[labels==1, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==2, 0], W[labels==2, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==3, 0], W[labels==3, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==4, 0], W[labels==4, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==5, 0], W[labels==5, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==6, 0], W[labels==6, 1], s=50, marker='o', color='green')\n",
    "plt.scatter(W[labels==7, 0], W[labels==7, 1], s=50, marker='o', color='green')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1ae41b",
   "metadata": {},
   "source": [
    "# 聚类效果可视化评估T-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fba55f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#方法二："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2ef97dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "be25ab76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "42e6f04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      148             72             35        0  33.6   \n",
       "1       85             66             29        0  26.6   \n",
       "2      183             64              0        0  23.3   \n",
       "3       89             66             23       94  28.1   \n",
       "4      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "01596976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st_Component</th>\n",
       "      <th>2n_Component</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.50283</td>\n",
       "      <td>0.947761</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.61838</td>\n",
       "      <td>-0.467979</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.224991</td>\n",
       "      <td>1.36553</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.62602</td>\n",
       "      <td>-0.958383</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.70654</td>\n",
       "      <td>-2.63184</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  1st_Component 2n_Component class\n",
       "0       1.50283     0.947761   NaN\n",
       "1      -1.61838    -0.467979   NaN\n",
       "2      0.224991      1.36553   NaN\n",
       "3      -1.62602    -0.958383   NaN\n",
       "4       2.70654     -2.63184   NaN"
      ]
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     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c62f96e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "38ac1089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dim1</th>\n",
       "      <th>Dim2</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.5166</td>\n",
       "      <td>-26.2587</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-8.785</td>\n",
       "      <td>16.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.22981</td>\n",
       "      <td>-37.6904</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-8.52195</td>\n",
       "      <td>14.0459</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.5668</td>\n",
       "      <td>-12.8456</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dim1     Dim2 class\n",
       "0  23.5166 -26.2587   NaN\n",
       "1   -8.785    16.23   NaN\n",
       "2  3.22981 -37.6904   NaN\n",
       "3 -8.52195  14.0459   NaN\n",
       "4  20.5668 -12.8456   NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3f1948e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8125d33",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
